Generating graphic visualizations that can be readily intuitive to a user presents significant challenges as there are countless variations in which data can be assembled and presented to the user. For example, while a type of visualization (e.g., a bar chart, a pie chart) may accommodate multiple dimensions of data (e.g., revenue, company, year), changes in the values or the numbers of the dimensions of data to be presented by the visualization often cause the presentation of the data to be difficult for the user to understand, for example, due to the type of visualization used or the manner in which the dimensions are represented by the various edges of the visualization. Further, the user may desire to view a same set of data under different types of visualizations. As there are many variations in which various visualization types can present the same set of data, there is a need to be able to determine how to present the data using a desired visualization type and in a manner that would be intuitively understandable to the user.